Embodiments of the inventive subject matter generally relate to the field of metaheuristic optimization computing, and, more particularly, to speculating in metaheuristic optimization computing.
Software tools employ metaheuristic optimization algorithms to solve optimization problems. Examples of metaheuristic optimization algorithms include evolutionary algorithms (e.g., genetic algorithm, differential evolution), ant colony optimization algorithms, simulated annealing algorithms, etc.
Evolutionary algorithms use techniques loosely based on Darwinian evolution and biological mechanisms to evolve solutions to tough design problems. A software tool that implements an evolutionary algorithm starts with a randomly generated population of solutions, and uses sexual recombination, crossover, mutation, and the Darwinian principles of natural selection to create new, more fit solutions. Evolutionary algorithms have been deployed in many aspects of research and development, and have generated human-competitive solutions to a wide range of problems. Within International Business Machines Corporation (IBM), (SNAP) has been successfully applied to I/O circuit design for Power7/7+, scan-chain routing, the high performance computing (HPC) bidding process, signal integrity for z-series buses, and compiler flag tuning. String-based genetic algorithms are very useful for exploring large, complex design spaces where other methods (e.g., linear regressions) fail.